Intel Corporation

Tahoe Research, Ltd.

Bastian Jaeger

Markus Achtelik

Daniel Pohl

Daniel Gurdan

Jan Stumpf

Jan Willem Vervoorst

Andre Ryll

According to various aspects, a collision avoidance method may include: receiving depth information of one or more depth imaging sensors of an unmanned aerial vehicle; determining from the depth information a first obstacle located within a first distance range and movement information associated with the first obstacle; determining from the depth information a second obstacle located within a second distance range and movement information associated with the second obstacle, the second distance range is distinct from the first distance range, determining a virtual force vector based on the determined movement information, and controlling flight of the unmanned aerial vehicle based on the virtual force vector to avoid a collision of the unmanned aerial vehicle with the first obstacle and the second obstacle.

Collision avoidance system, depth imaging system, vehicle, map generator and methods thereof

According to various aspects, an obstacle map generator is provided, including: one or more processors configured to receive one or more depth images from a depth imaging system, determine, for each depth image of the one or more received depth images, a first set of pixels and a second set of pixels, each pixel of the first set of pixels has a depth value assigned thereto and each pixel of the second set of pixels has no depth value assigned thereto or has a depth value outside a predefined depth value range assigned thereto, assign a pre-defined depth value to one or more pixels of the second set of pixels, and generate an obstacle map based on the determined first set of pixels and the one or more pixels of the second set of pixels having the pre-defined depth value assigned thereto.

Collision avoidance system, depth imaging system, vehicle, obstacle map generator, and methods thereof

Unmanned aerial vehicle-based systems and related methods for aerial vehicle-based systems and methods for generating landscape models are disclosed herein. An example unmanned aerial vehicle includes a communicator to receive an instruction for the unmanned aerial vehicle to fly over an area of interest. The example unmanned aerial vehicle includes a camera to generate sensor data for the area of interest. The example unmanned aerial vehicle includes data generator to generate a three-dimensional model of the area of interest based on the sensor data. The communicator is to communicate the three-dimensional model to a vehicle.

Unmanned aerial vehicle-based systems and methods for generating landscape models

Unmanned aerial vehicle-based systems and methods for agricultural landscape modeling are disclosed herein. An example unmanned aerial vehicle includes a communicator to receive an instruction to request the unmanned aerial vehicle to fly over an area of interest. The instruction is from a vehicle in the area of interest. The unmanned aerial vehicle is to fly over the area of interest. The example unmanned aerial vehicle includes a camera to generate image data for the area of interest. The example unmanned aerial vehicle includes a data generator to generate a vegetation landscape model of the area of interest based on the image data. The communicator is to communicate the vegetation landscape model to the vehicle.

Unmanned aerial vehicle-based systems and methods for agricultural landscape modeling

According to various aspects, a vehicle localization system is provided, including: a plurality of localization devices configured to receive vehicle position information representing a position of one or more vehicles located in a vicinity of the plurality of localization devices, the plurality of localization devices includes a first set of localization devices and a second set of localization devices, wherein the first set of localization devices is configured to receive additional position information representing a position of one or more localization devices of the second set of localization devices; and one or more processors configured to receive reference position information representing a positional relationship of the localization devices of the first set of localization devices relative to one another, determine a current position of each of the one or more vehicles based on the vehicle position information, the reference position information, and the additional position information.

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Munich, Germany

Bastian Jaeger